docstr: Down/up samples the input to the given size. The algorithm used for
  interpolation is determined by mode.
  
  Parameters
  ----------
  self
      Input array, Must have the shape
      [batch x channels x [optional depth] x [optional height] x width].
  size
      Output size.
  mode
      Interpolation mode. Can be one of the following:
      - linear
      - bilinear
      - trilinear
      - nearest
      - area
      - tf_area
      - bicubic
      - mitchellcubic
      - lanczos3
      - lanczos5
      - gaussian
  scale_factor
      Multiplier for spatial size that defines the output size
      (overwriting `size`).
  align_corners
      If True, the corner pixels of the input and output tensors are aligned,
      and thus preserving the values at the corner pixels. If False, the corner
      pixels are not aligned, and the interpolation uses edge value padding for
      out-of-boundary values.
      only has an effect when mode is 'linear', 'bilinear',
      'bicubic' or 'trilinear'. Default: False
  antialias
      If True, antialiasing is applied when downsampling an image.
      Supported modes: 'bilinear', 'bicubic'.
  out
      Optional output array, for writing the result to. It must
      have a shape that the inputs broadcast to.
  
  Returns
  -------
      resized array
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code: def interpolate(
      self,
      size: Union[Sequence[int], int],
      /,
      *,
      mode: Union[
          Literal[
              "linear",
              "bilinear",
              "trilinear",
              "nearest",
              "area",
              "nearest_exact",
              "tf_area",
              "bicubic",
          ]
      ] = "linear",
      scale_factor: Optional[Union[Sequence[int], int]] = None,
      recompute_scale_factor: Optional[bool] = None,
      align_corners: Optional[bool] = None,
      antialias: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Down/up samples the input to the given size. The algorithm used for
      interpolation is determined by mode.
  
      Parameters
      ----------
      self
          Input array, Must have the shape
          [batch x channels x [optional depth] x [optional height] x width].
      size
          Output size.
      mode
          Interpolation mode. Can be one of the following:
          - linear
          - bilinear
          - trilinear
          - nearest
          - area
          - tf_area
          - bicubic
          - mitchellcubic
          - lanczos3
          - lanczos5
          - gaussian
      scale_factor
          Multiplier for spatial size that defines the output size
          (overwriting `size`).
      align_corners
          If True, the corner pixels of the input and output tensors are aligned,
          and thus preserving the values at the corner pixels. If False, the corner
          pixels are not aligned, and the interpolation uses edge value padding for
          out-of-boundary values.
          only has an effect when mode is 'linear', 'bilinear',
          'bicubic' or 'trilinear'. Default: False
      antialias
          If True, antialiasing is applied when downsampling an image.
          Supported modes: 'bilinear', 'bicubic'.
      out
          Optional output array, for writing the result to. It must
          have a shape that the inputs broadcast to.
  
      Returns
      -------
          resized array
      """
      return ivy.interpolate(
          self._data,
          size,
          mode=mode,
          scale_factor=scale_factor,
          recompute_scale_factor=recompute_scale_factor,
          align_corners=align_corners,
          antialias=antialias,
          out=out,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
label_desc: Use image node_id 13 for calling the _ArrayWithLayersExperimental obj's underlying member method code with example usage: obj.interpolate() and returns: ivy